Affect recognition in real life scenarios

This source preferred by Theodoros Kostoulas

This data was imported from DBLP:

Authors: Kostoulas, T., Ganchev, T. and Fakotakis, N.

Editors: Esposito, A., Esposito, A.M., Martone, R., Müller, V.C. and Scarpetta, G.

https://doi.org/10.1007/978-3-642-18184-9

Volume: 6456

Pages: 429-435

Publisher: Springer

ISBN: 978-3-642-18183-2

This data was imported from Scopus:

Authors: Kostoulas, T., Ganchev, T. and Fakotakis, N.

Volume: 6456 LNCS

Pages: 429-435

ISBN: 9783642181832

DOI: 10.1007/978-3-642-18184-9_37

Affect awareness is important for improving human-computer interaction, but also facilitates the detection of atypical behaviours, danger, or crisis situations in surveillance and in human behaviour monitoring applications. The present work aims at the detection and recognition of specific affective states, such as panic, anger, happiness in close to real-world conditions. The affect recognition scheme investigated here relies on an utterance-level audio parameterization technique and a robust pattern recognition scheme based on the Gaussian Mixture Models with Universal Background Modelling (GMM-UBM) paradigm. We evaluate the applicability of the suggested architecture on the PROMETHEUS database, implemented in a number of indoor and outdoor conditions. The experimental results demonstrate the potential of the suggested architecture on the challenging task of affect recognition in real world conditions. However, further enhancement of the affect recognition performance would be needed before any deployment of practical applications. © 2011 Springer.

The data on this page was last updated at 04:57 on May 24, 2019.